A dark target Kalman filter algorithm for aerosol property retrievals in urban environment using multispectral images

Natural and anthropogenic aerosol atmospheric emissions play a fundamental role in directly modulating the incoming solar radiation and affecting the air quality, especially in large metropolitan regions. Likewise, aerosols indirectly impact cloud lifetime, atmospheric column thermodynamics and precipitation patterns. For these reasons, it is of particular importance to assess the aerosol spatial and temporal variability in the first instance to reduce the associated global climate models uncertainty to correctly forecasting future scenarios and then to react fast in applying mitigation strategies. In this paper, an aerosol optical depth (AOD) retrieval algorithm for high-spatial resolution images in the blue wavelength range for urban environments is developed for the first time. The proposed approach is completely blind because does not use look-up-tables or complex radiative transfer models, which require the setting/estimation of many parameters. The multi-wavelength (exploiting the coastal and the blue bands) AOD retrieval permits to retrieve also important aerosol micro-physical properties, e.g., the size. The proposed method leverages on the use of Kalman filters to deal with the unavoidable sensor's noise improving the accuracy of the estimation of the AOD. The approach is assessed on four different test cases acquired by Landsat 8 involving two metropolitan areas. A strong agreement to ground-based AERONET measurements is observed on several performance metrics. Clear advantages in comparison with the baseline approach relied upon the simple inversion of the explored model are pointed out.

Automated summary

Natural and anthropogenic aerosol emissions have a significant impact on solar radiation, air quality, cloud lifetime, and precipitation patterns. Therefore, accurately assessing aerosol variability is crucial for forecasting future scenarios and implementing mitigation strategies. This paper presents a novel aerosol optical depth retrieval algorithm for urban environments, which leverages multi-wavelength data and Kalman filters to improve estimation accuracy. The proposed method demonstrates strong agreement with ground-based measurements and outperforms the baseline approach.